Crack classification in rotor-bearing system by means of wavelet transform and deep learning methods: an experimental investigation

This paper presents an overview of the development of a reliable bearing system for a new line of small turbochargers where the bearing system has to be compatible with a new compressor and turbine design. The first part demonstrates how the increased weight of the turbine, due to a 40 % increase in flow capacity, influences the dynamic stability of the rotor-bearing system. The second part shows how stability can be improved by optimizing important floating ring parameters and by applying different bearing designs, such as profiled bore bearings supported on squeeze film dampers. Test results and stability analyses are included as well as the criteria which led to the decision to choose a squeeze film backed symmetrical 3-lobe bearing for this new turbocharger design.

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An experimental investigation for crack detection in rotor-bearing system

Journal of Environmental Science Computer Science and Engineering & Technology ◽

10.24214/jecet.c.8.4.30313 ◽

2019 ◽

Vol 8 (4) ◽

Keyword(s):

Experimental Investigation ◽

Crack Detection ◽

Rotor Bearing ◽

Bearing System

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A Wavelet Transform-Assisted Convolutional Neural Network Multi-Model Framework for Monitoring Large-Scale Fluorochemical Engineering Processes

Processes ◽

10.3390/pr8111480 ◽

2020 ◽

Vol 8 (11) ◽

pp. 1480

Author(s):

Xintong Li ◽

Kun Zhou ◽

Feng Xue ◽

Zhibing Chen ◽

Zhiqiang Ge ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Wavelet Transform ◽

Time Delay ◽

Convolutional Neural Network ◽

Dynamic Monitoring ◽

Model Framework ◽

Learning Methods ◽

Monitoring Method ◽

Useful Signal

The barely satisfactory monitoring situation of the hypertoxic fluorochemical engineering processes requires the application of advanced strategies. In order to deal with the non-linear mechanism of the processes and the highly complicated correlation among variables, a wavelet transform-assisted convolutional neural network (CNN) based multi-model dynamic monitoring method was proposed. A preliminary CNN model was first trained to detect faults and to diagnose part of them with minimum computational burden and time delay. Then, a wavelet assisted secondary CNN model was trained to diagnose the remaining faults with the highest possible accuracy. In this step, benefitting from the scale decomposition capabilities of the wavelet transform function, the inherent noise and redundant information could be filtered out and the useful signal was transformed into a higher compact space. In this space, a well-designed secondary CNN model was trained to further improve the fault diagnosis performance. The application on a refrigerant-producing process located in East China showed that not only regular faults but also hard to diagnose faults were successfully detected and diagnosed. More importantly, the unique online queue assembly updating strategy proposed remarkably reduced the inherent time delay of the deep-learning methods. Additionally, the application of it on the widely used Tennessee Eastman process benchmark strongly proved the superiority of it in fault detection and diagnosis over other deep-learning methods.

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Experimental Investigation on Vibration Control of Rotor-bearing System with Active Magnetic Exciter

Chinese Journal of Mechanical Engineering ◽

10.3901/cjme.2011.06.1013 ◽

2011 ◽

Vol 24 (06) ◽

pp. 1013 ◽

Cited By ~ 7

Author(s):

Weimin WANG

Keyword(s):

Experimental Investigation ◽

Vibration Control ◽

Rotor Bearing ◽

Bearing System

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Dynamic Effect of Bearing Wear on Rotor-Bearing System Response

Journal of Vibration and Acoustics ◽

10.1115/1.4007264 ◽

2013 ◽

Vol 135 (1) ◽

Cited By ~ 16

Author(s):

Athanasios C. Chasalevris ◽

Pantelis G. Nikolakopoulos ◽

Chris A. Papadopoulos

Keyword(s):

Wavelet Transform ◽

Dynamic Effect ◽

Journal Bearings ◽

System Response ◽

Continuous Wavelet ◽

Wear Model ◽

Main Target ◽

Rotor Bearing ◽

Frequency Components ◽

Bearing System

A rotor-bearing system is simulated in this study to investigate the effect of worn journal bearings on the system response and to specify the eventual development of additional frequency components. The well-known symmetric Dufrane bearing wear model is used here. The main target here is the investigation of the wear influence on the system response. An experimental layout was constructed for the needs of the current research, including an artificially worn bearing. It was observed that sub- and superharmonics are revealed in the continuous wavelet transform (CWT) of the rotor-bearing system response for worn bearings.

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Experimental Investigation of Pedestal Looseness in a Rotor-Bearing System

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.413-414.599 ◽

2009 ◽

Vol 413-414 ◽

pp. 599-605 ◽

Cited By ~ 12

Author(s):

Wen Xiu Lu ◽

Fu Lei Chu

Keyword(s):

Experimental Investigation ◽

Nonlinear Vibration ◽

Nonlinear Response ◽

Harmonic Component ◽

Vibration Characteristics ◽

Experimental Setup ◽

Rotor Bearing ◽

Harmonic Components ◽

Bearing System

An experimental setup of rotor-bearing system is installed and vibration characteristics of the system with pedestal looseness are investigated. The pretightening bolt between the bearing house and pedestal is adjusted to simulate the pedestal looseness fault. The vibration waveforms, spectra and orbits are used to analyze the nonlinear response of the system with pedestal looseness. Different parameters, including speed, looseness gap, imbalance mass and disk position are changed to observe the nonlinear vibration characteristics. The experiments show that the system motion generally contains the 1/2X fractional harmonic component and multiple harmonic components such as 2X, 3X, etc. Under some special conditions, the pedestal looseness occurs intermittently, that is, occurs in some periods and doesn’t in other periods.

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Deep Dehazing Network for Remote Sensing Image with Non-Uniform Haze

Remote Sensing ◽

10.3390/rs13214443 ◽

2021 ◽

Vol 13 (21) ◽

pp. 4443

Author(s):

Bo Jiang ◽

Guanting Chen ◽

Jinshuai Wang ◽

Hang Ma ◽

Lin Wang ◽

...

Keyword(s):

Remote Sensing ◽

Deep Learning ◽

Wavelet Transform ◽

Large Scale ◽

Remote Sensing Image ◽

Superior Performance ◽

Learning Technology ◽

Remote Sensing Images ◽

Learning Methods ◽

Atmospheric Scattering

The haze in remote sensing images can cause the decline of image quality and bring many obstacles to the applications of remote sensing images. Considering the non-uniform distribution of haze in remote sensing images, we propose a single remote sensing image dehazing method based on the encoder–decoder architecture, which combines both wavelet transform and deep learning technology. To address the clarity issue of remote sensing images with non-uniform haze, we preliminary process the input image by the dehazing method based on the atmospheric scattering model, and extract the first-order low-frequency sub-band information of its 2D stationary wavelet transform as an additional channel. Meanwhile, we establish a large-scale hazy remote sensing image dataset to train and test the proposed method. Extensive experiments show that the proposed method obtains greater advantages over typical traditional methods and deep learning methods qualitatively. For the quantitative aspects, we take the average of four typical deep learning methods with superior performance as a comparison object using 500 random test images, and the peak-signal-to-noise ratio (PSNR) value using the proposed method is improved by 3.5029 dB, and the structural similarity (SSIM) value is improved by 0.0295, respectively. Based on the above, the effectiveness of the proposed method for the problem of remote sensing non-uniform dehazing is verified comprehensively.

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